题号题目核心考点496下一个更大元素 I单调栈 + Map 缓存739每日温度下一个更大 + 存索引算距离503下一个更大元素 II循环数组（2 倍长度 + 取模）1019链表中的下一个更大节点链表转数组 + 模板1944队列中可以看到的人数弹栈计数 + 栈顶是否可见1475商品折扣后的最终价格右侧第一个 ≤ cur901股票价格跨度上一个更大（正序）402移掉 K 位数字单调递增栈 + k 限制853车队排序 + 时间比较（可不用栈）581最短无序连续子数组边界 + 最值扩展（非纯单调栈）
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В Финляндии предупредили об опасном шаге ЕС против России09:28
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Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.